Push notification delivery system with feedback analysis

ABSTRACT

A push notification delivery system includes a server system including a processor, a network interface, and memory storing program instructions having code segments for receiving a received push notification, code segments for determining at least one of a favorable push time and a favorable message format based upon a database of received push information developed from a plurality of prior sent push notifications, and code segments for pushing the message to the destination in accordance with the at least one of a favorable push time and a favorable message format. A method for delivering push notifications includes receiving a received push notification including a message and a destination, sending a sent push notification derived from the received push notification to the destination in accordance with at least one favorable condition, receiving received push information related to the sent push notification, and storing the received push information in a database.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 14/244,585, filed on Apr. 3, 2014, and entitled “PushNotification Delivery System with Feedback Analysis,” which is acontinuation of U.S. patent application Ser. No. 13/160,226 (now U.S.Pat. No. 8,731,523), entitled “Push Notification Delivery System withFeedback Analysis,” filed on Jun. 14, 2011, both of which areincorporated herein by reference in their entirety for all purposes.

BACKGROUND OF THE INVENTION

Push technology (aka “server push”) is a type of internet-basedcommunication where the request for a given transaction is initiated bya “publisher” or server. It is contrasted with pull technology where therequest for transmission of information is initiated by a receivingdevice or “client.”

Push services are sometimes based upon information preferences expressedin advance. This is referred to as a “publish/subscribe” model. Forexample, a client might subscribe to one or more information “channels.”The server associated with the channels can then push information to theclient when new content becomes available.

There are many types of push services. For example, synchronousconferencing and instant messaging are forms of push services.Increasingly popular are push-enabled web applications including marketdata distribution (e.g. stock tickers), online chat/messaging systems(e.g. “webchat”), auctions, online betting and gambling, sports results,monitoring consoles and sensor network monitor.

There are also hybrid push/pull systems. For example, email begins as apush system in that the SMTP protocol upon which it is based is a pushprotocol. However, the last step in the delivery of an email, e.g. froma mail server to a desktop computer, typically uses a pull protocol suchas POP3 or IMAP.

As will be appreciated, there are many and widespread uses for pushtechnology. However, one drawback of such technology is that it is,essentially, a unidirectional technology where pushes are sent todevices and then forgotten. This limits the opportunity to improve theeffectiveness of future pushes by deriving feedback as to theeffectiveness of prior pushes.

SUMMARY

A method for delivering push notifications, set forth by way of exampleand not limitation, includes receiving a received push notificationincluding a message and a destination, determining for the received pushnotification at least one of a favorable push time and a favorablemessage format based upon a database of received push informationdeveloped from a plurality of prior sent push notifications, and sendinga sent push notification derived from the received push notification tothe destination in accordance with the at least one of a favorable pushtime and a favorable message format. Sending push notifications at afavorable push time and/or in a favorable message format can improvedthe effectiveness of the push notification.

A push notification delivery system, set forth by way of example and notlimitation, includes a server system including a processor, memory, anda network interface. By way of non-limiting example, the memory storesprogram instructions having code segments for receiving a received pushnotification including a message and a destination, code segments fordetermining for the received push notification at least one of afavorable push time and a favorable message format based upon a databaseof received push information developed from a plurality of prior sentpush notifications, and code segments for pushing the message to thedestination in accordance with the at least one of a favorable push timeand a favorable message format.

A non-transient computer readable medium including program instructionsincludes, by way of example and not limitation, code segments forreceiving a received push notification including a message and adestination, code segments for determining for the received pushnotification at least one of a push time and a message format based upona database of received push information developed from a plurality ofprior sent push notifications, and code segments for pushing the messageto the destination in accordance with the at least one of a push timeand a message format.

A method for delivering push notifications, set forth by way of exampleand not limitation, includes receiving a received push notificationincluding a message and a destination. sending a sent push notificationderived from the received push notification to the destination inaccordance with at least one favorable condition, receiving receivedpush information related to the sent push notification, and storing thereceived push information in a database. Storing the received pushinformation in a database permits, by way of non-limiting example, theuse of a number of data mining techniques to determine factors which canimprove the push notification delivery process.

These and other embodiments and advantages and other features disclosedherein will become apparent to those of skill in the art upon a readingof the following descriptions and a study of the several figures of thedrawing.

BRIEF DESCRIPTION OF THE DRAWINGS

Several examples will now be described with reference to the drawings,wherein like components are provided with like reference numerals. Theexamples are intended to illustrate, but not to limit, various aspectsof inventions disclosed herein. In the drawings:

FIG. 1 is a diagram depicting an example system for delivering pushnotifications;

FIG. 2 is a block diagram of an example notification server system 12 ofFIG. 1;

FIG. 3 is a block diagram of an example device 20 of FIG. 1;

FIG. 4 is an illustration of example processes and operations of thenotification server system 12 of FIG. 1;

FIG. 5 is a flow diagram of an example process of the notificationserver system 12 of FIG. 1;

FIG. 6 is a flow diagram of an example SEND PUSH AT OPTIMAL CONDITIONprocess of FIG. 5;

FIG. 7 is a flow diagram of an example APPLY OPTIMIZATION FILTER BASEDON DATABASE process of FIG. 6;

FIG. 8 is a flow diagram of an example STORE RECEIVED PUSH INFORMATIONprocess of FIG. 5;

FIG. 9 is graph illustrating an optimization process, set forth by wayof example and not limitation; and

FIG. 10 is a graph further illustrating an optimization process, setforth by way of example and not limitation.

DETAILED DESCRIPTIONS

FIG. 1 is a diagram depicting, by way of non-limiting example, a system10 for delivering push notifications. In this example, a notificationserver system 12 is coupled to the Internet 14 for TCP/IP communication.As will be appreciated by those of skill in the art, the server system12 may include one or more servers.

Stored in memory of notification server system 12 are programinstructions including a number of code segments for implementingvarious processes of the server system 12. For example, the notificationserver system 12 includes code segments for receiving a received pushnotification 15 from a push originator server system 16 (such as acustomer website) via Internet 14. The received push notification caninclude a message and a destination, for example.

Example notification server system 12 can also include code segments forcreating a send token and a sent push notification, e.g. a sent pushnotification 17, derived from the received push notification 15. Thesend token can be used to distinguish the sent push notification fromother push notifications. For example, the sent push notification 17with send token can be sent over the Internet to a push gateway serversystem 18. The push gateway server system 18 can then send, for example,a push notification to a mobile device 20. A mobile device 20 can be, byway of non-limiting examples, a cell phone, a smart phone, a mobiletexting device, a tablet computer, etc. In other embodiments, a sentpush notification 17′I17″ with send token can be sent to otherdestinations such as a client computer, proxy server, device, etc.

In this example, gateway server system 18 is typically provided by aprovider implementing push notification protocols which are particularto, for example, a certain type of mobile device 20. For example,iPhone® and Android® mobile devices (e.g. smart phones, tabletcomputers, etc.) have dedicated push gateways maintained by Apple, Inc.and Google, Inc., respectively. It should be noted that the push gatewayserver system 18 may be part of notification server system 12 or may beseparate, as shown in FIG. 1.

In the example of FIG. 1, code segments are also included innotification server system 12 for receiving received push information 19concerning a processing of the sent push notification. These codesegments can, for example, identify the sent push notification from thereceived push information 19 by the send token. For example, receivedpush information can be developed by a mobile device 20 which is coupledto, by way of non-limiting example, a cellular network 22 and from thereto the Internet 14, such as by an Internet Service Provider (ISP) 24.TCP/IP protocol communications can thus occur between mobile device 20and notification server system 12 including the received pushinformation 19.

It will be appreciated that mobile device 20 is just one example of adevice or “client” that is receptive to push notifications. For example,a portable device 26 can communicate through Internet 14 via an ISP 24′using a WiFi or cable connection. Other devices 28, such personalcomputers, tablets, etc. can also communicate with the Internet 14 viaan ISP 24″. Alternatively, devices receptive to push notifications maybe coupled directly to Internet 14. In any event, the devices, howeverthey may be coupled to the Internet, are capable of communicating withnotification server system 12 to provide, for example, received pushinformation 19.

Thus, despite the different ways in which they are coupled to Internet14, portable device 26, other device 28 and still other devices, systemsand apparatus not shown can participate in the receiving of pushnotifications and the providing of information concerning a processingof a push notification in much the same way described previously withrespect to the example of mobile device 20. By way of furthernon-limiting examples, portable device 26, other device 28 and stillother devices, systems and apparatus may receive sent push notifications17 without the intermediary of a push gateway server system 18.

According to certain example embodiments, notification server system 12receives a push notification including a message and a destination frompush originator server system 16. Upon receiving the push notification,notification server system 12 can create. for example, a push token. Asused herein, a “push token” is an identifier used to represent thereceived push notification and is suitable to be used as a key inperforming lookups to retrieve information pertaining to the receivedpush notification identified by the push token. The push token may bederived in some way from the received push notification and/or metadatarelated thereto (e.g. a hash) or may be otherwise generated or assigned(e.g. it may be randomly assigned).

In this example, an operation is performed by which the recipients ofthe push message are identified. Further, push messages are generatedwhich are addressed to the one or more recipients of the pushnotification and which preferably include another identifier referred toas a “send token.” As used herein, a “send token” is an identifier usedto represent a sent push notification and is suitable to be used as akey in performing lookups to retrieve information pertaining to the sentpush notification identified by the send token. The send token may belinked to the sent push notification or may be unrelated to the sentpush notification.

The send token, in this example, is therefore used to represent the sentpush notification. The send token may also be associated with the pushtoken on a one-to-one or many-to-one basis. That is, a single push tokenmay be associated with a single send token or with multiple send tokens.In such an arrangement, it is possible to retrieve information about thereceive push notification associated with the push token using the sendtoken as a key, and also possible to, given a push token, retrieveinformation about the sent push messages associated with one or moreassociated send tokens. It is therefore apparent that a one-to-manymapping may exist between the push token and the send tokens and thatthis mapping can be used to obtain related information given a pushtoken or a send token.

By way of non-limiting example, a sent push notification 17, 17′ or 17″derived from a received push notification 15 and a send token is sentover the Internet 14 to a designated destination. For example, pushgateway server system 18 is configured to forward the sent pushnotification 17 to its destination, e.g. mobile device 20 or othermobile device, by non-limiting examples. Notification server system 12is disposed to receive push information 19, 19′ or 19″ concerning aprocessing of the sent push notification as identified by the relatedsend token.

The send token can be, for example, stored in a memory of notificationserver system 12 to identify the sent push notification. Also, thereceived push information can be stored, for example, in a memory ofnotification server system 12. The received push notification and/or thesent push notification can also be stored in a memory of notificationserver system 12, for example.

By way of further example, a push token can be created after receivingthe received push notification and can be subsequently stored in amemory of notification server system 12. The sent push notification canbe sent to one or more destinations. In this example, a number of sendtokens are created corresponding to the number of destinations. It willtherefore be apparent that there can be a one-to-one or many-to-onemapping between the send token(s) and the push token.

By way of non-limiting example, program instructions stored in a memoryof notification server system 12 can include code segments for receivinga received push notification including a message and a destination, codesegments creating a send token, code segments sending a sent pushnotification derived from the received push notification and the sendtoken, and code segments receiving received push information which isidentified by the send token concerning a processing of the sent pushnotification. In this example, the program instructions can furtherinclude code segments for creating a push token after receiving thereceived push notification. By still further example, the programinstructions can include code segments for storing in a persistentdatabase of notification server system 12 at least one of a receivedpush notification, a sent push notification, a send token and a pushtoken.

By way of example and not limitation, a device or “client,” such as amobile device 20, portable device 26 or other device 28, may includeprogram instructions stored in a memory which include code segments forreceiving a push notification with send token, code segments forprocessing the push notification and code segments for sending pushinformation which is identified by the send token concerning theprocessing of the push notification. Such push information can be sentto, for example, notification server system 12 to provide feedback fromthe device as to the processing of the push notification.

By way of non-limiting example, code segments for processing the pushnotification can be associated with an application program running onthe client. For example, code segments for processing the pushnotification can include a library of code segments which can beutilized by the application program. Further, the push information, inthis example, includes information concerning the use of the pushnotification by the application program. The push information may beaugmented by other information, such as life cycle event informationpertaining to the device or an application program of the device. Suchlife cycle information may be sent separately or may in some casesaccompany the push information.

FIG. 2 is a block diagram depicting certain example embodiments of, forexample, a notification server system 12 of FIG. 1 which comprises atleast one processor 30, which may be a multi-core processor and mayinclude a cache memory, coupled to a bus 32. Also coupled to bus 32 is aROM 34 which typically contains program instructions comprising a BIOSof notification server system 12. A network interface 36 is coupledbetween bus 32 and a network (e.g. the Internet). Volatile memory 38 iscoupled to bus 32 to act as a fast random access memory (RAM) ofprocessor 30. In addition to processor 30, other devices coupled to bus32 may access volatile memory 38 using, for example, DMA transfers. Asused herein, “volatile memory” will refer to memory that loses data whenits power source is removed.

Non-volatile memory 40 is, by way of non-limiting example, coupled tobus 32 and can serve as storage for the various program instructions anddata for notification server system 12. As used herein, “non-volatilememory” shall refer to a non-volatile computer readable medium such as ahard disk drive, optical drive, flash memory or other computer readablememory which does not lose data when power is removed. In certainexamples some or all of the non-volatile memory remote or networkattached storage.

Both volatile memory and non-volatile memory are examples ofnon-transitory computer readable media. As used herein, “non-transitorycomputer readable media” refers to a physical memory and excludespropagating electromagnetic waves and/or other forms of non-statutorysubject matter in accordance with current USPTO protocols.

According to certain example embodiments, non-volatile memory 40 willstore program instructions comprising an operating system fornotification server system 12. The operating system may be UNIX, Linux,Windows, or another operating system, as will be appreciated by those ofskill in the art. Further, a portion of the storage capacity ofnon-volatile memory 40 may be used to store one or more databases andassociated code segments. Of course, operating system code segments,databases and associated code segments, etc. may also be stored involatile memory for as long as it is provided with power.

I/O subsystem 42 is coupled to bus 32 and handles the various input andoutput operations of notification server system 12, which may include,by way of example and not limitation, output to a display, input from akeyboard, input from a pointing device, etc. Such devices may be coupledpart-time to I/O subsystem 42. Such devices may also be coupled via thenetwork interface and a network which may be Internet 14 or another LANor WAN. This allows for remote control and monitoring of notificationserver system 12.

It should be noted that the notification server system 12 of FIG. 1 maycomprise multiple servers, load balancers, etc. Therefore, as usedherein, a “notification server system” comprises one or more serversconfigured to implement the processes described herein. The notificationserver system 12 depicted in FIG. 2 is but one example hardwareconfiguration for a notification server system.

FIG. 3 is a block diagram depicting certain example embodiments of adevice such as mobile device 20 of FIG. 1. In this example, mobiledevice 20 includes a processor 44, which may be a multi-core processorand may include a cache memory, which is coupled to bus 46. Also coupledto bus 46, in this example, is input device subsystem 48 which comprisesvarious input devices, which may include, by way of example and notlimitation, a pointing device, such as a touch screen, a keyboard inputdevice, which may be physical or software generated (virtual), amicrophone for audio input, a camera for the input of still images orvideo, and various other sensors. Input device subsystem 48 of mobiledevice 20 may have more or fewer devices depending on design choice,cost, etc.

A display subsystem 50 of mobile device 20 is coupled to bus 46 and mayinclude, by way of example and not limitation, a flat panel display suchas an LCD display, electronic paper display, etc., an adapter to driveother attachable displays, etc. Also, display subsystem 50 of mobiledevice 20 may include an electro-acoustic transducer (aka “speaker” or“earphone”) for audio output.

In this example, a network interface 52, coupled to bus 46, providesaccess to a cellular network 22. This is typically accomplished with abidirectional RF digital communication link 53. A memory 54 serves, inthis example, as a random access (RAM) memory of processor 44.

In addition to processor 44, other devices coupled to bus 46 may accessmemory 54 (which may be volatile memory) using, for example, DMAtransfers. A non-volatile memory 56 (e.g. “flash” memory) is coupled tobus 46 in certain examples, which can provide storage for programinstructions for an operating system of mobile device 20, as well asprogram instructions for the various applications of mobile device 20.

FIG. 4 is an illustration of certain computer implemented processesperformed by the example notification server system 12. The processes ofnotification server system 12 are responsive to: received pushnotification 62 from a push source such as, for example, push originatorsystem 16 of FIG. 1; a received push information 66 from, for example, aclient 20, 26 or 28; optional application (“app”) life cycle events 68from a client; and push source interface events 78 from a push source,such as push originator system 16. The processes of notification serversystem 12 are also illustrated to communicate with a database and/orqueue 74, which may also form a part of the notification server 12. Thenotification server system 12 also provides a sent push notification 76,as will be explained in more detail subsequently.

By way of non-limiting example, the received push notification can bereceived by an API call from push originator server system 16 of FIG. 1.These API calls can be made, by way of a non-limiting example, using aREST based APL As well known to those of skill in the art, “REST” is anacronym for Representational State Transfers and is a way of organizingstate transitions of and communications between computers (clients andservers) communicating in a network environment, typically using HTTP orHTTPS protocol. REST based operations often include the ability tocreate, read, update and delete resources specified by a hierarchicalspecifier in a URL. For example, an API call comprising a received pushnotification can be as follows:

push <message> <application> <recipient> <options>

recipient can be a device GUID or a tag which refers to a stored list ofdevice GUIDs

<push token> is added as a named field for tracking

After processing a received push notification 62, the notificationserver system 12 sends a sent push notification 76 to, for example, thepush gateway server system 18 of FIG. 1. For example, a pushnotification can implement the following format:

-   -   push <message> <send token>

Notification server system 12 can also, for example, be responsive to apush source interface 78 which comprises, for example, a REST based APIto be used to query statistical information concerning the use of acertain application which may be stored in database and queue subsystem74, as well as many other kinds of queries, information uploadoperations, information update operations, and information deletionoperations. By way of further example, notification server system 12 isalso responsive to received push information 66 and/or app life cycleevents 68, typically from clients.

Notification server system 12 can, for example, also include otherprocesses such as processes to develop statistics from data stored indatabase and/or queue 74 such as received push information 66 and applife cycle events 68. For example, statistics can be developed such asthe number of times a certain application program is opened,initialized, closed, foregrounded, backgrounded, etc. on one or moreclients. An example of received push information 66 takes the followingformat:

-   -   push receive event <token> <payload>        An example of an app life cycle event 68 takes the following        format:        life cycle event <payload>        the payload may contain information specifying what happened on        the device like “started this application”, etc.

A still further example process that can be performed by notificationserver system 12 is to periodically scan database and/or queue 74 fordata that requires processing. After identifying such data, processescan be initiated to, for example, enqueue a job to process the databaseand/or queue, typically to create derived data products.

FIG. 5 is a flow diagram depicting a method 80 for delivering pushnotifications. The process begins with an operation 82 and continues inan operation 84 wherein a determination is made as to whether a receivedpush notification has been received. Such received push notificationsare typically the result of an API call from a push source that is usinga web based API to make calls to a REST based web interface. Thereceived push notification contains, for example, a message, adestination, and associated metadata. If in operation 84 a received pushnotification has not been received, then the process continues with are-invocation of operation 84.

If it is determined in operation 84 that a received push notificationhas been received the process continues with an operation 86 in which asent push notification is sent at an optimal condition. As used herein“optimal” may be used synonymously with “favorable”, “improved” or thelike to mean a condition which is meant to improve the result of a push.By way of non-limiting example, an “optimal condition” can be afavorable push notification time and/or a favorable push notificationformat. For example, a favorable push notification time could be justbefore a mealtime for a push notification having to do with arestaurant. As another example, a favorable push format may be to changethe color, font size and/or font of the display of a push.

After the push notification has been sent by operation 86 an operation88 stores and processes received push information. This occurs, forexample, as a result of received push information being received from adevice such as a phone or other device to which the push notificationwas sent. Such received push information may be the result of a useropening an application in response to a push notification that hasresulted in passive indicia associated with an application of the deviceor an active notification such as modal dialog on the device inassociation with the application. The received push information. in thisnon-limiting example, can be stored and processed so that subsequentsent push notifications can be improved. Upon completion of operation88, the process continues with operation 84 to await a new pushnotification.

FIG. 6 is a flow diagram depicting operation 86 of FIG. 5 in greaterdetail. The process begins at 90 and continues in an operation 92wherein a received push notification is parsed to develop a parsedreceived push notification. Then, in an operation 94, a determination ismade as to whether or not to optimize a sent push notification. Thisdetermination may be made, for example, based on information in the pushnotification in conjunction with information in a database. For example,a certain application or “app” may be associated with information thatis needed immediately. The database may contain information that saysthat received push notifications associated with this app must bedelivered immediately. In other examples, a device, associated user,message type, message content etc. may all be used to determine whetheror not one or more of the sent push notification(s) associated with agiven received push notification may be optimized.

Since a given received push notification may result in multiple sentpush notifications, operation 92 may be invoked multiple times for asingle invocation of operation 86. If it is determined in operation 94that a sent push notification should not be optimized, then that sentpush notification is processed in operation 96. Operation 96 sends thesent push notification. Once the sent push notification is sent, theprocess with respect to that sent push notification ends at 98. Othersent push notifications associated with the parsed received pushnotification may still be in various stages of processing in the processof FIG. 6.

If it is determined in operation 94 that a sent push notification shouldbe optimized, then the process continues in an operation 100 whichapplies a filter based upon the received push information in thedatabase of received push information developed from a plurality ofprior sent push notifications in order to provide, for example, afavorable push time, a favorable push format, or both, for the sent pushnotification. One or more parameters derived from the received pushnotification associated with the sent push notification may be used inthe determination of a favorable push time, a favorable push format, orboth. Then, in an operation 102, a determination is made as to whetherthe sent push notification should be personalized.

If it is determined in operation 102 that the sent push notificationshould be personalized, then the process continues in an operation 104which modifies the sent push notification. Such modification alters thepresentation of the push notification in some way, resulting in afavorable push format. By way of non-limiting example, such alterationmay include altered or additional visual display, altered or additionalmessage content, altered or additional sound or vibration or acombination of alterations. The process then continues with operation106 which determines whether or not the sent push notification can bescheduled.

If, in operation 106, it is determined that the sent push notificationcan be scheduled, then the process continues in an operation 108 whichplaces the sent push notification in a send queue. The process thencontinues in an operation 110 which determines whether the messagedensity is acceptable. That is, if at the current time (e.g. as providedby a real-time clock) the destination of the sent push notification hasbeen saturated with sent push notifications to such a degree thatfurther sent push notifications could annoy the user (and possibly causehim to turn off push notifications) then the density would be deemedunacceptable. Thus, if it is determined in operation 110 that thedensity is acceptable, then the process continues in operation 96described earlier and the process is complete at 98.

FIG. 7 is a flow diagram depicting a process of operation 100 of FIG. 6in greater detail. The process 100 of FIG. 7 may operate on more thanone received push notification. Thus, multiple invocations of theprocess 100 of FIG. 7 may occur as a result of multiple received pushinformation being available to be processed at the time of invocation ofthe process 100. If these operations are performed concurrently, care ispreferably taken to ensure that the database of received pushinformation developed from a plurality of prior sent push notificationsis consistent with respect to the information being processed. Thusvarious data locking mechanisms may be employed to ensure the integrityof the database of received push information developed from a pluralityof prior sent push notifications.

The process 100 begins at 112 and continues with an operation 114 whichdetermines if a full feature set for an app associated with a givenreceived push notification is available in the database of received pushinformation. If it is determined in operation 114 that a full featureset is not available, then the process continues with operation 116which uses a general purpose “global” feature set as a starting pointfor features that are missing but for which some initial value isneeded. This addresses the “cold start” condition present in systemsthat perform iterative refinement of a database, and which use thedatabase during the process. Once initialized, the app feature set willgradually adapt based on received push information and additional deviceinformation observed over time. Upon completion of operation 116assigning the global feature set to be used initially for features thatare missing for this app/user combination, the process continues withoperation 118.

Operation 118 causes the sent push notification to conform to a localfavorable condition reflected in the database of received pushinformation for this app/user combination. The process then continueswith an operation 120 which sets individualization and schedulingparameters for this sent push notification. The process is thencompleted at 122. If it was determined in operation 114 that the app hasa full feature set. then the process 100 skips operation 116 andcontinues directly with operation 118.

FIG. 8 illustrates, by way of non-limiting example, operation 88 of FIG.5 in greater detail. Process 88, in this example, begins at 124 and, inan operation 126, received push information is stored in a database. Byway of non-limiting example, the received push information can be storedin a database 74 of FIG. 4. Next, in an operation 128, an app featureset can be updated based upon database analysis. The process 88 iscompleted at 130.

It will be appreciated that the database analysis of operation 128 canbe performed using a variety of data analysis techniques. By way ofnon-limiting example, set of data analysis techniques grouped under theumbrella term “data mining” are useful in that data mining techniquesfocus on modeling and knowledge discovery for predictive rather thanpurely descriptive purposes.

Data mining is an interdisciplinary field of computer science whereinpatterns are extracted from large data sets by combining methods fromstatistics, artificial intelligence and database management. Data miningalso encompasses data dredging, data fishing and data snooping whereindata mining techniques are used to sample parts of a larger populationdata set that are (or may be) too small for reliable statisticalinference to be made about the validity of any patterns discovered. Datadredging, data fishing and data snooping techniques can, however, beused in creating new hypotheses to test against larger data populationsor to be tested for validity in a more empirical fashion.

As will be appreciated by those of skill in the art, data mining caninclude one or more methods and algorithms. By way of non-limitingexamples, these methods and algorithms may include association rulelearning, cluster analysis, constructive induction, data analysis.decision trees, factor analysis, knowledge discovery, neural nets,predictive analytics, reactive business intelligence, regression,structured data analysis (statistics), and text mining.

By way of non-limiting example, a useful data mining technique is“cluster analysis” or “'clustering.” With cluster analysis, a set ofobservations (including, for example. received push information from thedatabase) are assigned into subsets known as “'clusters” so thatobservations in the same cluster are similar in some sense. An importantstep in most clustering techniques is to select a distance measure whichwill determine the similarity of two observations or “elements” iscalculated.

Decision tree techniques encompass decision support tools which use atree-like graph or model of decisions and their possible consequences,including chance event outcomes. resource costs and utility. Decisiontrees can be used in decision analysis to help identify a strategy mostlikely to reach a goal, e.g. the optimization of a push notification.

Factor analysis is a statistical method used to describe variabilityamong observed variables in terms of a potentially lower number ofunobserved variables called factors. For example, variations in three orfour observed variables may reflect the variations in a singleunobserved variable, or in a reduced number of unobserved variables.Factor analysis searches for such joint variations in response tounobserved latent variables.

Knowledge discovery is a concept in the field of computer science thatdescribes the process of automatically searching large volumes of datafor patterns that can be considered knowledge about the data. It iscategorized according to what kind of data is searched and in what formthe result of the search is represented.

An artificial neural network (ANN), often simply called a neural network(NN), is a computational model that is inspired by the structure and/orfunctional aspects of biological neural networks. A neural networkincludes an interconnected group of artificial neurons (e.g. programmedconstructs that mimic properties of biological neurons) and it processesinformation using a connectionist approach to computation. In mostcases, an ANN is an adaptive system that changes its structure based onexternal or internal information that flows through the ANN during alearning phase. Modem ANN can typically be considered to be non-linear,statistical data modeling tools and are often used to model complexrelationships between inputs and output to find patterns in the data.

Predictive analytics encompass a variety of statistical techniques frommodeling, data mining and game theory that analyze current andhistorical facts to make predication about future events. Models formedfrom predictive analytic techniques can capture relationships among manyfactors to allow an assessment of risk and reward associated with aparticular set of conditions to guide in a decision making process.

Regression analysis encompasses statistical techniques which canestimate relationships among variables. Types of regression analysisinclude linear regression models, simple linear regression, logisticregression, nonlinear regression, nonparametric regression, robustregression and stepwise regression.

Text mining, sometimes referred to as “text data mining”, refers to theprocess of deriving high-quality information from text (e.g. in a pushmessage). High-quality information may be derived through theidentification of patterns and trends by, for example, a statisticalpattern learning process. Text mining usually involves the process ofstructuring the input text (usually parsing, along with the addition ofsome derived linguistic features and the removal of others, with thesubsequent insertion into a database), deriving patterns within thestructured data and evaluating and interpreting the output.

FIG. 9 is a graph depicting a data multi-dimensional feature space 132depicted in simplified form as a two dimensional space by way ofnon-limiting example. Received push information regarding previous sentpush notifications may be collected and stored. Some sent pushnotifications may be “converted” through user interaction in response tothe sent push notification. When a sent push notification is converted,received push information may be received from the device and stored inthe database.

Many parameters may be collected which can become feature vectors in themulti-dimensional space. Various derivative representations of the datamay be developed by processes which use the received push information inthe database as input, and produce derivative data structures which canmap the feature vectors into a form that can be used to determine afavorable time or favorable format for subsequent sent pushnotifications, by way of non-limiting examples.

Initially, in this example, the multi-dimensional space 132 may bepopulated by features that are obtained from an aggregate ofdemographically or otherwise similar users. The points in this initialset of feature vectors may form “clusters” of points which representsent push notifications that have been successfully “converted” throughuser interaction as a result of a push. In some instances, an inferencemay be made that a user interaction with a given app is the result of apush notification when the interaction is shortly after a pushnotification.

Favorable clusters of aggregate feature vectors 134 are depicted in FIG.9, by way of non-limiting example. Thus, sent push notifications thatare within this cluster in multi-dimensional space will be sent, in thisexample. Push notifications that are outside of this cluster may, insome cases, be adjusted so that they are within the cluster and thealtered sent push notification may then be sent. The feature set of anew device application combination, in this example, is measured againstknown clusters of device and application features. At any given time inthe system, there will be a number of clusters that represent similardevice and application combinations. When a new device is observed. itsmulti-dimensional feature set is compared using one or more distancemetrics including, but not limited to, Euclidean distance, Levenshteindistance, or Mahalanobis distance. Using multiple distance calculations,a composite distance for a new device from one of the existing clustersand associate that device with the nearest cluster can be derived, inthis example. Based on the example association, optimized pushes to thenew application device combination can be delivered as would deliveriesto the rest of the cluster in the near term.

In FIG. 10, a new device DX may be measured for affinity to twodifferent clusters C 1 and C2, by way of non-limiting example. Eachcluster will have an attraction based on the composite distancemeasurement. This initial association is only for new device andapplication combinations. Over time the weights and ranks of features inan application device combination will be personalized based on userresponse to pushes.

Over time, with the addition of more feature vectors that are particularto the specific user and application associated with a push notificationwill populate the space, and, in certain embodiments, they will displacefeature vectors associated with the aggregate. In other examples,feature vectors weight of importance will fade over time. Newer featurevectors may cause a highly favorable region to drift over time to anarea of multi-dimensional space that is more personalized to thespecific application and user.

The new personalized favorable area 136 is shown in FIG. 9 by way ofnon-limiting example. Sent push notifications that are within thiscluster in multi-dimensional space can, for example, be sent. Pushnotifications that are outside of this cluster may in some cases beadjusted so that they are within the cluster and the altered sent pushnotification can then be sent.

A further example process for optimizing a push notification deliverysystem with feedback analysis follows. In this non-limiting example,received push information regarding previous sent push notifications iscollected and stored in a database. Some sent push notifications are“converted” through user interaction in response to the sent pushnotification, and others are not. When a sent push notification isconverted, received push information can be received from the device andstored in the database. Many parameters can be collected which may berepresented as feature vectors in a multi-dimensional space. Variousderivative representations of the data may developed by processes whichuse the received push information in the database as input and canproduce derivative data structures which map the feature vectors into aform that can be used to determine a favorable time or favorable formatfor subsequent sent push notifications.

One such structure is a “K-D tree” which can organize vast amounts ofinformation into a multi-dimensional data structure that can be rapidlyqueried, by way of non-limiting example. The K-D tree is so namedbecause it supports an arbitrarily high number (K) of dimensions. Oneexample K-D tree would store feature vectors for each sent pushnotification, whether or not the sent push notification was successfullyconverted. Those vectors that represent a successfully converted sentpush notification will represent a location in the multi-dimensionalspace where it is was successful to send a sent push notification, andwill have a re-enforcing effect for future sent push notifications.Those feature vectors that represent a sent push notification that wasnot successfully converted represent a location in multidimensionalspace where it was not successful to send a sent push notification, inthis non-limiting example. These locations will have anegative-reinforcing effect for future sent push notifications.

Over time “hot” and “cold” areas are developed in the multi-dimensionalspace, in this example. Hot areas represent portions of themulti-dimensional space where it is favorable to send a pushnotification, while cold areas represent portions of themulti-dimensional space where it is undesirable to send a pushnotification.

As new data is collected for a given region of the multi-dimensionalspace, old data is associated with a lower “weight” relative to that ofthe new data. In certain embodiments, the relevance of data is taperedoff on a logarithmic scale. This is considered advantageous, in thisexample, because the initial set of feature vectors for themulti-dimensional space are derived not from personal information, sincenone are available at the outset, but from general information about anaggregate of users that are demographically or otherwise similar to thisuser.

In this example, the K-D tree is queried for data surrounding a pointfor which the system proposes to send a push notification. The queryspace can be, for example, a hypersphere surrounding the point. Allfeature vectors within that hypersphere are returned as the queryresult. These feature vectors each have either a positive or negativeeffect on the favorability of sending a push notification correspondingto the given point in multidimensional space. An overall metric offavorability is developed for this point in multi-dimensional spacebased upon the feature vectors that are returned.

Each feature is associated with a “weight” that can be developed using,for example, a Pearson correlation relating the feature to successfulconversion, by way of non-limiting example. Such weights can be used ina number of ways to adjust their respective features' importance in themetric of favorability. For example, the scale of the correspondingdimensions can be adjusted so that less important data is spread over agreater area in the multi-dimensional space. Alternatively, the weightcan be used to affect the impact of the associated feature vector on theoverall favorability metric of a given point.

Since a richer set of information is available for successfulconversions than for unsuccessful, there exists an imbalance in terms ofthe number of dimensions in which the two kinds of information can existin the multi-dimensional space. It may therefore be desirable, in thisexample, to replicate the unsuccessful feature vectors so that theyoccupy multiple locations along the axes for which no data is availableor applicable.

Engagement with an application at various times of day independently ofa sent push notification can have a reinforcing effect in the portionsof multi-dimensional space that represent that interaction. This kind ofinteraction can therefore make previously “cold” areas of themulti-dimensional space turn “hot.” This can be advantageous in that thepresence of cold areas in the multi-dimensional space may have aself-reinforcing effect. e.g. since an area is so cold that no sent pushnotifications are ever sent, dynamic change in that area requires someoutside event such as independent engagement with the application.

The aforementioned overall favorability metric associated with a givenpoint in K-D space, in this example, may be deemed to warrant sending asent push notification at the time and or manner associated with thatpoint in multi-dimensional space. When this occurs, a search for a moreacceptable point may be conducted. Various other locations in the spacecan be queried, and associated favorability metrics can be obtained.Such points may represent future times or alternative means ofpresentation or both. When one or more acceptable point is found, a“best” one (e.g. the one considered to be the most favored) can beselected, and the sent push notification can be scheduled for delivery.It is often desirable to favor an acceptable point that is associatedwith a relatively nearby time over points that are far in the future.

In this example, maintenance of the K-D tree can include the removal ofold data or the rescaling of a dimension. It may therefore be desirableto rebuild the tree periodically. Alternatively, the tree may be editedto reflect new data or new weights. Other processes may also oralternatively be performed, as will be appreciated by those of skill inthe art.

It will be appreciated that the forgoing systems and processes includecomputer implemented processes. As such, a number of different hardwareand software platforms may be used to implement the systems andprocesses described herein. The processes are generally stored asprogram instructions including code segments in non-transitory computerreadable media that can be accessed, directly or indirectly, by systemsand/or devices as disclosed herein or otherwise. Furthermore, althoughvarious embodiments have been described using specific terms, wordsand/or phrases, such description is for illustrative purposes only. Theterms, words and/or phrases used are for the purpose of descriptionrather than of limitation.

It is to be understood that changes and variations may be made by thoseof ordinary skill in the art without departing from the spirit or thescope of the present invention, which is set forth in the followingclaims. In addition, it should be understood that aspects of variousother embodiments may be interchanged either in whole or in part. It istherefore intended that the claims be interpreted in accordance with thetrue spirit and scope of the invention without limitation or estoppel.

What is claimed is:
 1. A system for delivering push notificationscomprising: a processor; and a memory storing instructions executable bythe processor, the instructions when executed cause the processor to:receive a message to be delivered via a specified application, anddestination information, wherein the destination information representsone or more target devices associated with a user to which a currentpush notification including the message should be delivered via thespecified application, obtain, from a data store, a set of userengagement information, wherein the user engagement informationcomprises information developed from at least one previous userinteraction with the specified application at a first device in the oneor more target devices associated with the user, process the obtainedset of user engagement information to determine at least one of a pushtime and a message format for the current push notification to bedelivered, and deliver the current push notification to at least one ofthe one or more target devices in accordance with the determination. 2.The system of claim 1, wherein the user engagement information includespush information associated with a conversion of a previously sent pushnotification through an instance of the specified application on thefirst device.
 3. The system of claim 1, wherein the instructions toprocess the obtained set of user engagement information compriseinstructions to process life cycle event information for the specifiedapplication on the first device, to determine at least one of a pushtime and a message format for the current push notification to bedelivered.
 4. The system of claim 3, wherein the life cycle eventinformation for the specified application on the first device comprisesstatistics about user interactions with the specified application on thefirst device aggregated over a specified duration of time.
 5. The systemof claim 4, wherein the statistics about user interactions with thespecified application are determined based on a number of times thespecified application program was opened, a number of times thespecified application program was initialized, a number of times thespecified application program was closed, a number of times thespecified application program was foregrounded, and a number of timesthe specified application program was backgrounded on at least one ofthe target devices.
 6. The system of claim 1, wherein the instructionsto deliver the current push notification in accordance with thedetermined push time and a message format comprise instructions thatwhen executed by the processor causes the processor to: determine, basedon a target device type, the user, a type of the received message, orcontent of the received message, that the current push notification isto be optimized prior to delivery; and modify, responsive to theinstructions to determine, a time of delivery of the push message to oneof the target devices or a presentation format for the current pushnotification, in accordance with a result of processing the userengagement information.
 7. The system of claim 1, wherein theinstructions to determine the push time for the current pushnotification further comprise instructions to determine a time ofdelivery of the push message to one of the target devices based on aresult of processing the user engagement information and based on adensity of other push notifications scheduled to be delivered to thetarget device.
 8. The system of claim 1, wherein the instructions todetermine the message format for the current push notification furthercomprise instructions to determine a presentation format for the currentpush notification, including altering visual display properties ormessage content of the received message based on a result of processingthe user engagement information.
 9. The system of claim 1, wherein theinstructions to determine the message format for the current pushnotification further comprise instructions to augment the receivedmessage with additional sound signaling, or additional vibrationalsignaling based on a result of processing the user engagementinformation.
 10. The system of claim 1, wherein the instructions toprocess the user engagement information includes at least one ofassociation rule learning, cluster analysis, constructive induction,data analysis, decision trees, factor analysis, knowledge discovery,neural nets, predictive analytics, reactive business intelligence,regression, structured data analysis (statistics), and text mining. 11.A computer-implemented method for delivering push notification messagescomprising: receiving, via a specified application executed by aprocessor, a message to be delivered and destination information, thedestination information representing one or more target devicesassociated with a user to which a current push notification includingthe message should be delivered via the specified application;obtaining, by the processor, from a data store, a set of user engagementinformation, the user engagement information comprising informationdeveloped from at least one previous user interaction with the specifiedapplication at a first device in the one or more target devicesassociated with the user; processing, by the processor, the obtained setof user engagement information to determine at least one of a push timeand a message format for the current push notification to betransmitted, and delivering, via the processor, the current pushnotification to at least one of the one or more target devices inaccordance with the determination.
 12. The computer-implemented methodof claim 11, wherein the user engagement information includes pushinformation associated with a conversion of a previously sent pushnotification through an instance of the specified application on thefirst device.
 13. The computer-implemented method of claim 11, whereinthe instructions to process the obtained set of user engagementinformation comprise instructions to process life cycle eventinformation for the specified application on the first device, todetermine at least one of a push time and a message format for thecurrent push notification to be delivered.
 14. The computer-implementedmethod of claim 11, wherein the instructions to deliver the current pushnotification in accordance with the determined push time and a messageformat comprise instructions that when executed by the processor causesthe processor to: determine, based on a target device type, the user, atype of the received message, or content of the received message, thatthe current push notification is to be optimized prior to delivery; andmodify, responsive to the instructions to determine, a time of deliveryof the push message to one of the target devices or a presentationformat for the current push notification, in accordance with a result ofprocessing the user engagement information.
 15. The computer-implementedmethod of claim 11, wherein the instructions to process the userengagement information includes at least one of association rulelearning, cluster analysis, constructive induction, data analysis,decision trees, factor analysis, knowledge discovery, neural nets,predictive analytics, reactive business intelligence, regression,structured data analysis (statistics), and text mining.
 16. A computerprogram product for delivering push notifications, the computer programproduct embodied in a non-transitory computer readable storage mediumand comprising computer instructions that when executed by a processorcauses the processor to: receive a message to be delivered via aspecified application, and destination information, wherein thedestination information represents one or more target devices associatedwith a user to which a current push notification including the messageshould be delivered via the specified application, obtain, from a datastore, a set of user engagement information, wherein the user engagementinformation comprises information developed from at least one previoususer interaction with the specified application at a first device in theone or more target devices associated with the user, process theobtained set of user engagement information to determine at least one ofa push time and a message format for the current push notification to bedelivered, and deliver the current push notification to at least one ofthe one or more target devices in accordance with the determination. 17.The non-transitory computer readable storage medium of claim 16, whereinthe user engagement information includes push information associatedwith a conversion of a previously sent push notification through aninstance of the specified application on the first device.
 18. Thenon-transitory computer readable storage medium of claim 16, wherein theinstructions to process the obtained set of user engagement informationcomprise instructions to process life cycle event information for thespecified application on the first device, to determine at least one ofa push time and a message format for the current push notification to bedelivered.
 19. The non-transitory computer readable storage medium ofclaim 16, wherein the instructions to deliver the current pushnotification in accordance with the determined push time and a messageformat comprise instructions that when executed by the processor causesthe processor to: determine, based on a target device type, the user, atype of the received message, or content of the received message, thatthe current push notification is to be optimized prior to delivery; andmodify, responsive to the instructions to determine, a time of deliveryof the push message to one of the target devices or a presentationformat for the current push notification, in accordance with a result ofprocessing the user engagement information.
 20. The non-transitorycomputer readable storage medium of claim 16, wherein the instructionsto process the user engagement information includes at least one ofassociation rule learning, cluster analysis, constructive induction,data analysis, decision trees, factor analysis, knowledge discovery,neural nets, predictive analytics, reactive business intelligence,regression, structured data analysis (statistics), and text mining.